Bioinformatics Advance Access published online on July 16, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn365
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Apparently low reproducibility of true differential expression discoveries in microarray studies
1 School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
2 Bioinformatics Centre and School of Life Science, University of Electronic Science and Technology of China, Chengdu, 610054, China
*To whom correspondence should be addressed. Prof. Zheng Guo, Xia Li, E-mail: guoz{at}ems.hrbmu.edu.cn, lixia{at}ems.hrbmu.edu.cn
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Abstract |
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Motivation: Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries.
Results: Based on a statistical model, we show that even in technical replicate tests using identical samples, it is highly likely that the selected DEG lists will be very inconsistent in the presence of small measurement variations. Therefore, the apparently low reproducibility of DEG detection from current technical replicate tests does not indicate low quality of microarray technology. We also demonstrate that heterogeneous biological variations existing in real cancer data will further reduce the overall reproducibility of DEG detection. Nevertheless, in small subsamples from both simulated and real data, the actual false discovery rate (FDR) for each DEG list tends to be low, suggesting that each separately determined list may comprise mostly true DEGs. Rather than simply counting the overlaps of the discovery lists from different studies for a complex disease, novel metrics are needed for evaluating the reproducibility of discoveries characterized with correlated molecular changes.
Contact: guoz{at}ems.hrbmu.edu.cn, lixia{at}ems.hrbmu.edu.cn
Associate Editor: Dr. Olga Troyanskaya
Min Zhang, Chen Yao, Jinfeng Zou and Lin Zhang contribute equally to this work
Received on April 14, 2008; revised on July 14, 2008; accepted on July 14, 2008
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